From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge

2024/10/02 by Xiaohe Wu, Wu, Xiefeng
Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Law #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2410.01458

openalex publication_date 2024/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Q-shaping is an extension of Q-value initialization and serves as an alternative to reward shaping for incorporating domain knowledge to accelerate agent training, thereby improving sample efficiency by directly shaping Q-values. This approach is both general and robust across diverse tasks, allowing for immediate impact assessment while guaranteeing optimality. We evaluated Q-shaping across 20 different environments using a large language model (LLM) as the heuristic provider. The results demonstrate that Q-shaping significantly enhances sample efficiency, achieving a 16.87% improvement over the best baseline in each environment and a 253.80% improvement compared to LLM-based reward shaping methods. These findings establish Q-shaping as a superior and unbiased alternative to conventional reward shaping in reinforcement learning.

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